Neural models have performed well on distantly supervised datasets for relation extraction. [1]}, [2]} used convolutional network with max-pooling on word embeddings for this task, whereas [3]}, [4]}, [5]} used word-level attention model for single-instance sentence-level relation extraction. [6]}, [7]}, [8]} used neural networks in a multi-instance setting to find a relation from a bag of independent sentences. Recently, graph convolutional network-based (GCN) [9]} models have become popular for many NLP tasks. These models work on non-linear graph structures. [10]}, [7]}, [12]}, [13]} used graph convolution networks for relation extraction. They consider each token in a sentence as a node in the graph and use a syntactic dependency tree to create a graph structure among the nodes. Recently, neural joint extraction approaches [14]}, [15]} were proposed for this task.
